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Bestandsnummer des Verk�ufers SEWV.0387791051.G

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Bibliografische Details

Titel: Statistical Methods for Quality Assurance: Basics, Measurement, Control, Capability, and Improvement (Springer Texts in Statistics)
Verlag: Springer
Erscheinungsjahr: 2016
Sprache: Englisch
ISBN-10: 0387791051
ISBN-13: 9780387791050
Einband: Softcover
Auflage: 2. Auflage
Zustand: good

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Inhaltsangabe

This undergraduate statistical quality assurance textbook clearly shows with real projects, cases and data sets how statistical quality control tools are used in practice. Among the topics covered is a practical evaluation of measurement effectiveness for both continuous and discrete data. Gauge Reproducibility and Repeatability methodology (including confidence intervals for Repeatability, Reproducibility and the Gauge Capability Ratio) is thoroughly developed. Process capability indices and corresponding confidence intervals are also explained. In addition to process monitoring techniques, experimental design and analysis for process improvement are carefully presented. Factorial and Fractional Factorial arrangements of treatments and Response Surface methods are covered.

Integrated throughout the book are rich sets of examples and problems that help readers gain a better understanding of where and how to apply statistical quality control tools. These large and realistic problem sets in combination with the streamlined approach of the text and extensive supporting material facilitate reader understanding.

Second Edition Improvements

Extensive coverage of measurement quality evaluation (in addition to ANOVA Gauge R&R methodologies)
New end-of-section exercises and revised-end-of-chapter exercises
Two full sets of slides, one with audio to assist student preparation outside-of-class and another appropriate for professors' lectures
Substantial supporting material

Supporting Material

Seven R programs that support variables and attributes control chart construction and analyses, Gauge R&R methods, analyses of Fractional Factorial studies, Propagation of Error analyses and Response Surface analyses
Documentation for the R programs
Excel data files associated with the end-of-chapter problem sets, most from real engineering settings

�ber die Autorin bzw. den Autor

Stephen Vardeman is Professor of Statistics and Industrial Engineering at Iowa State University. He is a Fellow of the American Statistical Association and an (elected) Ordinary Member of the International Statistical Institute. His interests include physical science and engineering applications of statistics, statistics and metrology, quality assurance, business applications of statistics, modern ("big") data analytics, statistical machine learning, directional data analysis, reliability, statistics education, and the development of new statistical theory and methods. He has published textbooks in both quality assurance and engineering statistics and recently developed effective new graduate-level coursework in modern statistical machine learning.

J. Marcus Jobe is Professor of Information Systems and Analytics in the Farmer School of Business at Miami University (Ohio). His current research interests focus on measurement quality, multivariate process monitoring, pattern recognition, and oil exploration applications. Professor Jobe has taught and conducted research in Ukraine, and he has won numerous teaching awards at Miami University, including the all-university Outstanding Teaching award and the Richard T. Farmer Teaching Excellence award for senior professors in the school of business. The U.S. Dept. of State twice awarded him the Senior Fulbright Scholar award (1996-1997 and 2005-2006). Marcus Jobe also works as a consultant and has co-authored two statistics texts with Stephen Vardeman.

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